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RichardErkhov/
berkeley-nest_-_Starling-LM-7B-alpha-gguf

Name: berkeley-nest_-_Starling-LM-7B-alpha-gguf
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Starling-LM-7B-alpha - GGUF

Model creator: https://huggingface.co/berkeley-nest/
Original model: https://huggingface.co/berkeley-nest/Starling-LM-7B-alpha/

Name	Quant method	Size
Starling-LM-7B-alpha.Q2_K.gguf	Q2_K	2.53GB
Starling-LM-7B-alpha.IQ3_XS.gguf	IQ3_XS	2.81GB
Starling-LM-7B-alpha.IQ3_S.gguf	IQ3_S	2.96GB
Starling-LM-7B-alpha.Q3_K_S.gguf	Q3_K_S	2.95GB
Starling-LM-7B-alpha.IQ3_M.gguf	IQ3_M	3.06GB
Starling-LM-7B-alpha.Q3_K.gguf	Q3_K	3.28GB
Starling-LM-7B-alpha.Q3_K_M.gguf	Q3_K_M	3.28GB
Starling-LM-7B-alpha.Q3_K_L.gguf	Q3_K_L	3.56GB
Starling-LM-7B-alpha.IQ4_XS.gguf	IQ4_XS	3.67GB
Starling-LM-7B-alpha.Q4_0.gguf	Q4_0	3.83GB
Starling-LM-7B-alpha.IQ4_NL.gguf	IQ4_NL	3.87GB
Starling-LM-7B-alpha.Q4_K_S.gguf	Q4_K_S	3.86GB
Starling-LM-7B-alpha.Q4_K.gguf	Q4_K	4.07GB
Starling-LM-7B-alpha.Q4_K_M.gguf	Q4_K_M	4.07GB
Starling-LM-7B-alpha.Q4_1.gguf	Q4_1	4.24GB
Starling-LM-7B-alpha.Q5_0.gguf	Q5_0	4.65GB
Starling-LM-7B-alpha.Q5_K_S.gguf	Q5_K_S	4.65GB
Starling-LM-7B-alpha.Q5_K.gguf	Q5_K	4.78GB
Starling-LM-7B-alpha.Q5_K_M.gguf	Q5_K_M	4.78GB
Starling-LM-7B-alpha.Q5_1.gguf	Q5_1	5.07GB
Starling-LM-7B-alpha.Q6_K.gguf	Q6_K	5.53GB

Original model description:

license: apache-2.0 datasets:

berkeley-nest/Nectar language:
en library_name: transformers tags:
reward model
RLHF
RLAIF

Starling-LM-7B-alpha

Developed by: Banghua Zhu * , Evan Frick * , Tianhao Wu * , Hanlin Zhu and Jiantao Jiao.
Model type: Language Model finetuned with RLHF / RLAIF
License: Apache-2.0 license under the condition that the model is not used to compete with OpenAI
Finetuned from model: Openchat 3.5 (based on Mistral-7B-v0.1)

We introduce Starling-7B, an open large language model (LLM) trained by Reinforcement Learning from AI Feedback (RLAIF). The model harnesses the power of our new GPT-4 labeled ranking dataset, berkeley-nest/Nectar, and our new reward training and policy tuning pipeline. Starling-7B-alpha scores 8.09 in MT Bench with GPT-4 as a judge, outperforming every model to date on MT-Bench except for OpenAI's GPT-4 and GPT-4 Turbo. We release the ranking dataset Nectar, the reward model Starling-RM-7B-alpha and the language model Starling-LM-7B-alpha on HuggingFace, and an online demo in LMSYS Chatbot Arena. Stay tuned for our forthcoming code and paper, which will provide more details on the whole process.

Starling-LM-7B-alpha is a language model trained from Openchat 3.5 with reward model berkeley-nest/Starling-RM-7B-alpha and policy optimization method advantage-induced policy alignment (APA). The evaluation results are listed below.

Model	Tuning Method	MT Bench	AlpacaEval	MMLU
GPT-4-Turbo	?	9.32	97.70
GPT-4	SFT + PPO	8.99	95.28	86.4
Starling-7B	C-RLFT + APA	8.09	91.99	63.9
Claude-2	?	8.06	91.36	78.5
GPT-3.5-Turbo	?	7.94	89.37	70
Claude-1	?	7.9	88.39	77
Tulu-2-dpo-70b	SFT + DPO	7.89	95.1
Openchat-3.5	C-RLFT	7.81	88.51	64.3
Zephyr-7B-beta	SFT + DPO	7.34	90.60	61.4
Llama-2-70b-chat-hf	SFT + PPO	6.86	92.66	63
Neural-chat-7b-v3-1	SFT + DPO	6.84	84.53	62.4
Tulu-2-dpo-7b	SFT + DPO	6.29	85.1

For more detailed discussions, please check out our blog post, and stay tuned for our upcoming code and paper!

Blog: https://starling.cs.berkeley.edu/
Paper: Coming soon!
Code: Coming soon!

Uses

Important: Please use the exact chat template provided below for the model. Otherwise there will be a degrade in the performance. The model output can be verbose in rare cases. Please consider setting temperature = 0 to make this happen less.

Our model follows the exact chat template and usage as Openchat 3.5. Please refer to their model card for more details. In addition, our model is hosted on LMSYS Chatbot Arena for free test.

The conversation template is the same as Openchat 3.5:

import transformers
tokenizer = transformers.AutoTokenizer.from_pretrained("openchat/openchat_3.5")

# Single-turn
tokens = tokenizer("GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant:").input_ids
assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747]

# Multi-turn
tokens = tokenizer("GPT4 Correct User: Hello<|end_of_turn|>GPT4 Correct Assistant: Hi<|end_of_turn|>GPT4 Correct User: How are you today?<|end_of_turn|>GPT4 Correct Assistant:").input_ids
assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747, 15359, 32000, 420, 6316, 28781, 3198, 3123, 1247, 28747, 1602, 460, 368, 3154, 28804, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747]

# Coding Mode
tokens = tokenizer("Code User: Implement quicksort using C++<|end_of_turn|>Code Assistant:").input_ids
assert tokens == [1, 7596, 1247, 28747, 26256, 2936, 7653, 1413, 334, 1680, 32000, 7596, 21631, 28747]

Code Examples

python

import transformers

tokenizer = transformers.AutoTokenizer.from_pretrained("berkeley-nest/Starling-LM-7B-alpha")
model = transformers.AutoModelForCausalLM.from_pretrained("berkeley-nest/Starling-LM-7B-alpha")

def generate_response(prompt):
    input_ids = tokenizer(prompt, return_tensors="pt").input_ids
    outputs = model.generate(
        input_ids,
        max_length=256,
        pad_token_id=tokenizer.pad_token_id,
        eos_token_id=tokenizer.eos_token_id,
    )
    response_ids = outputs[0]
    response_text = tokenizer.decode(response_ids, skip_special_tokens=True)
    return response_text

# Single-turn conversation
prompt = "Hello, how are you?"
single_turn_prompt = f"GPT4 Correct User: {prompt}<|end_of_turn|>GPT4 Correct Assistant:"
response_text = generate_response(single_turn_prompt)
print("Response:", response_text)

## Multi-turn conversation
prompt = "Hello"
follow_up_question =  "How are you today?"
response = ""
multi_turn_prompt = f"GPT4 Correct User: {prompt}<|end_of_turn|>GPT4 Correct Assistant: {response}<|end_of_turn|>GPT4 Correct User: {follow_up_question}<|end_of_turn|>GPT4 Correct Assistant:"
response_text = generate_response(multi_turn_prompt)
print("Multi-turn conversation response:", response_text)

### Coding conversation
prompt = "Implement quicksort using C++"
coding_prompt = f"Code User: {prompt}<|end_of_turn|>Code Assistant:"
response = generate_response(coding_prompt)
print("Coding conversation response:", response)

License

The dataset, model and online demo is a research preview intended for non-commercial use only, subject to the data distillation License of LLaMA, Terms of Use of the data generated by OpenAI, and Privacy Practices of ShareGPT. Please contact us if you find any potential violation.

Acknowledgment

We would like to thank Wei-Lin Chiang from Berkeley for detailed feedback of the blog and the projects. We would like to thank the LMSYS Organization for their support of lmsys-chat-1M dataset, evaluation and online demo. We would like to thank the open source community for their efforts in providing the datasets and base models we used to develope the project, including but not limited to Anthropic, Llama, Mistral, Hugging Face H4, LMSYS, OpenChat, OpenBMB, Flan and ShareGPT.

Citation

@misc{starling2023,
    title = {Starling-7B: Improving LLM Helpfulness & Harmlessness with RLAIF},
    url = {},
    author = {Zhu, Banghua and Frick, Evan and Wu, Tianhao and Zhu, Hanlin and Jiao, Jiantao},
    month = {November},
    year = {2023}
}

RichardErkhov/berkeley-nest_-_Starling-LM-7B-alpha-gguf